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The introduction of Airborne Laser Scanning (ALS) to forests has been revolutionary during the last decade. This development was facilitated by combining earlier ranging lidar discoveries [1–5], with experience obtained from full-waveform ranging radar [6,7] to new airborne laser scanning systems which had

The introduction of Airborne Laser Scanning (ALS) to forests has been revolutionary during the last decade. This development was facilitated by combining earlier ranging lidar discoveries [1–5], with experience obtained from full-waveform ranging radar [6,7] to new airborne laser scanning systems which had components such as a GNSS receiver (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and a scanning mechanism. Since the first commercial ALS in 1994, new ALS-based forest inventory approaches have been reported feasible for operational activities [8–12]. ALS is currently operationally applied for stand level forest inventories, for example, in Nordic countries. In Finland alone, the adoption of ALS for forest data collection has led to an annual savings of around 20 M€/year, and the work is mainly done by companies instead of governmental organizations. In spite of the long implementation times and there being a limited tradition of making changes in the forest sector, laser scanning was commercially and operationally applied after about only one decade of research. When analyzing high-ranked journal papers from ISI Web of Science, the topic of laser scanning of forests has been the driving force for the whole laser scanning research society over the last decade. Thus, the topic “laser scanning in forests” has provided a significant industrial, societal and scientific impact. [...]
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The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (~80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (khat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (khat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (~90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ~30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms. The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.
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Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring

Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction.
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We report here on the observation and offline detection of the weak tsunamis generated by earthquakes near Indonesia on 11 April 2012 using radar systems and tide gauges on the coasts of Sumatra and the Andaman Islands. This work extends the previous observations

We report here on the observation and offline detection of the weak tsunamis generated by earthquakes near Indonesia on 11 April 2012 using radar systems and tide gauges on the coasts of Sumatra and the Andaman Islands. This work extends the previous observations of the much stronger 2011 Japan tsunami. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends primarily on the bathymetry: the wider the shallow continental shelf, the greater this time. The weak Indonesia tsunamis were detected successfully in spite of the narrow shallow-water shelf offshore from the radar systems. Larger tsunamis could obviously be detected further from the coast. This paper provides further confirmation that radar is an important tool to aid in tsunami observation and warning.
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Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices,

Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.
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Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft or

Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft or the energy required by the sensors for radio transmission. Planning is hampered by the complex vehicle and communication dynamics and by uncertainty in the locations of sensors, so we develop a technique based on model-free learning. We present a stochastic optimisation method that allows the data-ferrying aircraft to optimise data collection trajectories through an unknown environment in situ, obviating the need for system identification. We compare two trajectory representations, one that learns near-optimal trajectories at low data requirements but that fails at high requirements, and one that gives up some performance in exchange for a data collection guarantee. With either encoding the ferry is able to learn significantly improved trajectories compared with alternative heuristics. To demonstrate the versatility of the model-free learning approach, we also learn a policy to minimise the radio transmission energy required by the sensor nodes, allowing prolonged network lifetime.
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The use of terrestrial remote imaging techniques, specifically LiDAR (Light Detection And Ranging) and digital stereo-photogrammetry, are widely proven and accepted for the mapping of geological structure and monitoring of mass movements. The use of such technologies can be limited, however: LiDAR generally

The use of terrestrial remote imaging techniques, specifically LiDAR (Light Detection And Ranging) and digital stereo-photogrammetry, are widely proven and accepted for the mapping of geological structure and monitoring of mass movements. The use of such technologies can be limited, however: LiDAR generally by the cost of acquisition, and stereo-photogrammetry by the tradeoff between possible resolution within the scene versus the spatial extent of the coverage. The objective of this research is to test a hybrid gigapixel photogrammetry method, and investigate optimal equipment configurations for use in mountainous terrain. The scope of the work included field testing at variable ranges, angles, resolutions, and in variable geological and climatologically settings. Original field work was carried out in Canada to test various lenses and cameras, and detailed field mapping excursions were conducted in Norway. The key findings of the research are example data generated by gigapixel photogrammetry, a detailed discussion on optimal photography equipment for gigapixel imaging, and implementations of the imaging possibilities for rockfall mapping. This paper represents a discussion about a new terrestrial 3-dimensional imaging technique. The findings of this research will directly benefit natural hazard mapping programs in which rockfall potential must be recorded and the use of standard 3-dimensional imaging techniques cannot be applied.
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Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 mm can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses t < ~6. This paper investigates the variations in radiances at these wavelengths over a deep convective cloud system for their potential to extend retrievals of t and ice particle size De to optically thick clouds. Measurements from an imager, an interferometer, the Cloud Physics Lidar (CPL), and the Cloud Radar System (CRS) aboard the NASA ER-2 aircraft during the NASA TC4 (Tropical Composition, Cloud and Climate Coupling) experiment flight during 5 August 2007, are used to examine the retrieval potential of infrared radiances over optically thick ice clouds. Simulations based on coincident in situ measurements and combined cloud t from CRS and CPL measurements are comparable to the observations. They reveal that brightness temperatures at these bands and their differences (BTD) are sensitive to t up to ~20 and that for ice clouds having t > 20, the 3.7–10.8 µm and 3.7–6.7 µm BTDs are the most sensitive to De. Satellite imagery appears to be consistent with these results suggesting that t and De could be retrieved for greater optical thicknesses than previously assumed. But, because of sensitivity of the BTDs to uncertainties in the atmospheric profiles of temperature, humidity, and ice water content, and sensor noise, exploiting the small BTD signals in retrieval algorithms will be very challenging.
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A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and

A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and without a rotation matrix and compared to the following field-measured forest biometric parameters: tree diameter, tree height and stand volume. The analytical results involving the rotation matrix correlated better than those without the rotation matrix even for natural scattering surfaces within the forests. Our primary finding was that all of the decomposition powers from the rotated matrix correlated significantly to the forest biometric parameters when divided by the total power. The surface scattering ratio of the total power markedly decreased with the forest growth, whereas the canopy and double-bounce scattering ratios increased. The observations of the decomposition powers were consistent with the tree growth characteristics. Consequently, we found a significant logarithmic relationship between the decomposition powers and the forest biometric parameters that can potentially be used to estimate the forest stand volume.
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In this study we use visible, short-wave infrared and thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data validated with high-resolution Quickbird (QB) and Worldview2 (WV2) for mapping debris cover in the eastern Himalaya using two independent approaches: (a) a decision tree

In this study we use visible, short-wave infrared and thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data validated with high-resolution Quickbird (QB) and Worldview2 (WV2) for mapping debris cover in the eastern Himalaya using two independent approaches: (a) a decision tree algorithm, and (b) texture analysis. The decision tree algorithm was based on multi-spectral and topographic variables, such as band ratios, surface reflectance, kinetic temperature from ASTER bands 10 and 12, slope angle, and elevation. The decision tree algorithm resulted in 64 km2 classified as debris-covered ice, which represents 11% of the glacierized area. Overall, for ten glacier tongues in the Kangchenjunga area, there was an area difference of 16.2 km2 (25%) between the ASTER and the QB areas, with mapping errors mainly due to clouds and shadows. Texture analysis techniques included co-occurrence measures, geostatistics and filtering in spatial/frequency domain. Debris cover had the highest variance of all terrain classes, highest entropy and lowest homogeneity compared to the other classes, for example a mean variance of 15.27 compared to 0 for clouds and 0.06 for clean ice. Results of the texture image for debris-covered areas were comparable with those from the decision tree algorithm, with 8% area difference between the two techniques.
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An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for

An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales.
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Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these

Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cover maps. This work evaluates the classification performance of a least square support vector machine (LS-SVM) algorithm with respect to areas of agreement and disagreement between two existing land cover maps. The approach involves the use of time series of Moderate-resolution Imaging Spectroradiometer (MODIS) 250-m Normalized Difference Vegetation Index (NDVI) (16-day composites) and gridded climatic indicators. LS-SVM is trained on reference samples obtained through visual interpretation of Google Earth (GE) high resolution imagery. The core of the training process is based on repeated random splits of the training dataset to select a small set of suitable support vectors optimizing class separability. A large number of independent validation samples spread over three contrasting regions in Europe (Eastern Austria, Macedonia and Southern France) are used to calculate classification accuracies for the LS-SVM NDVI-derived LC map and for two (globally available) LC products: GLC2000 and GlobCover. The LS-SVM LC map reported an overall accuracy of 70%. Classification accuracies ranged from 71% where GlobCover and GLC2000 agreed to 68% for areas of disagreement. Results indicate that existing LC products are as accurate as the LS-SVM LC map in areas of agreement (with little margin for improvements), while classification accuracy is substantially better for the LS-SVM LC map in areas of disagreement. On average, the LS-SVM LC map was 14% and 18% more accurate compared to GlobCover and GLC2000, respectively.
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The main purpose of this study is to develop a new Windows-based program that calculates a quality control parameter that shows the quality of GPS observations using Global Positing Sensing (GPS) data in a Receiver INdependent Exchange (RINEX) format. This new program, Global

The main purpose of this study is to develop a new Windows-based program that calculates a quality control parameter that shows the quality of GPS observations using Global Positing Sensing (GPS) data in a Receiver INdependent Exchange (RINEX) format. This new program, Global Positing Sensing Quality Control) (GPSQC), allows general GPS users to easily and intuitively check the quality of GPS observations before post-processing, which will lead to the improvement of GPS positioning precision in diverse areas of GPS applications. The GPSQC is designed to control the multi-path, cycle slip, and ionospheric errors of L1 and L2 signals in GPS observations. The GPSQC was developed using C#.NET language for the Window series with Microsoft Graphical User Interfaces (MS GUIs). This program gives brief information for GPS observations, time series plots, graphs of quality control parameters, and a summary report in MS word, Excel and PDF formats. It can simply perform quality checking of GPS observations that is difficult for surveyors conducting field work. We expect that GPSQC can be used to improve the accuracy of positioning and to solve time-consuming problems due to data loss and large errors in GPS observations.
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Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day.

Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R2: 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R2: 0.80, RMSE: 1.8 K for 500 m; R2: 0.82, RMSE: 1.6 K for 1,000 m).
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Establishment of protected areas (PA) has been one of the leading tools in biodiversity conservation. Globally, these kinds of conservation interventions have given rise to an increase in PAs as well as the need to empirically evaluate the impact of these PAs on

Establishment of protected areas (PA) has been one of the leading tools in biodiversity conservation. Globally, these kinds of conservation interventions have given rise to an increase in PAs as well as the need to empirically evaluate the impact of these PAs on forest cover. Few of these empirical evaluations have been geared towards comparison of pre and post policy intervention landscapes. This paper provides a method to empirically evaluate such pre and post policy interventions by using a cellular automata-Markov model. This method is tested using remotely sensed data of Bannerghatta National park (BNP) and its surrounding, which have experienced various national level policy interventions (Indian National Forest Policy of 1988) and rapid land cover change between 1973 and 2007. The model constructs a hypothetical land cover scenario of BNP and its surroundings (1999 and 2007) in the absence of any policy intervention, when in reality there has been a significant potential policy intervention effect. The models predicted a decline in native forest cover and an increase in non forest cover post 1992 whereas the actual observed landscape experienced the reverse trend where after an initial decline from 1973 to 1992, the forest cover in BNP is towards recovery post 1992. Furthermore, the models show a higher deforestation and lower reforestation than the observed deforestation and reforestation patterns for BNP post 1992. Our results not only show the implication of national level policy changes on forest cover but also show the usefulness of our method in evaluating such conservation efforts.
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The worldwide waning health of coral reefs implies an increasing need for monitoring them at colony scale over large areas. Relaying fieldwork considerably, the remote sensing approach can address this need in offering spectral information relevant for coral health detection with 0.5 m

The worldwide waning health of coral reefs implies an increasing need for monitoring them at colony scale over large areas. Relaying fieldwork considerably, the remote sensing approach can address this need in offering spectral information relevant for coral health detection with 0.5 m spatial accuracy. We investigated the potential of spectral diversity indices to achieve the discrimination of coral-dominated assemblages and health states from novel satellite imagery (WorldView-2, WV2). Both Equitability’s (E) and Pielou’s (P) operators were used to quantify the evenness of the corrected visible spectral bands (two times 26 combinations of five bands) corresponding to remotely sensed colonies. Three scleractinian corals (Porites lobata, P. rus and Acropora pulchra) that are primarily involved in Moorea’s reef building (French Polynesia) were examined in respect to their health state (healthy or unhealthy, referring to both bleached and dead coral, hereinafter). Using four classifiers, we showed that the Support Vector Machine (SVM) greatly discerned among the six coral classes based upon the five WV2 spectral bands (93%), thus surpassing the classification issued from the three traditionally used bands (80%). Coupling the WV2 dataset with Egreen-red, Eyellow-red or E“coastal”-blue-green allowed the SVM performance to attain 96%. On the other hand, adding the E“coastal”-blue to the WV2-dataset contributed to a substantially increase of the classification accuracy derived from the Random Forest classifier, stepping from 64% to 77%. Significant contributions of spectral diversity indices to surveying coral health were further discussed in the light of spectral properties of coral-related pigments. These findings may play a major role for the extensive monitoring of coral health states at a fine scale, and for the management and restoration of damaged coral reefs.
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Abstract
This book provides a state-of-the-art overview of satellite archaeology and it is an invaluable volume for archaeologists, scientists, and managers interested in using satellite Earth Observation (EO) to improve the traditional approach for archaeological investigation, protection and management of Cultural Heritage.
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The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the

The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of −0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset.
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